Quality of Analytical Measurements: Univariate Regression

Ortiz, M.C.; Sánchez, M.S.; Sarabia, L.A.

doi:10.1016/b978-044452701-1.00091-0

Cited by 16 publications

(36 citation statements)

References 51 publications

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“…For n-way data, these values can be computed with the procedure in Refs. [57,60]. For the case at hand, simazine, CCa varies from 0.81 to 1.15 mg kg À1 and CCb from 1.55 to 2.21 mg kg À1 in x 0 ¼ 0 in matrix matched and standards, respectively.…”

Section: Quantification and Identification With Ptv-gc-ms Data In Thementioning

confidence: 99%

Usefulness of PARAFAC for the Quantification, Identification, and Description of Analytical Data

Ortiz

Sarabia

Sánchez

et al. 2015

Data Handling in Science and Technology

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Section: Quantification and Identification With Ptv-gc-ms Data In Thementioning

confidence: 99%

Usefulness of PARAFAC for the Quantification, Identification, and Description of Analytical Data

Ortiz

Sarabia

Sánchez

et al. 2015

Data Handling in Science and Technology

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“…6 Frequently, this term is used interchangeably with the concept of standardization, although that is a distinct idea. Standardization means to characterize the response of an instrument according to the known properties of the material, and this is usually done by the 'calibration curve' (which should be called a 'standardization curve').…”

Section: Defining Calibrationmentioning

confidence: 99%

“…In fact, what is usually done is to standardize the response by means of a series of samples of known concentrations. 6 Accordingly, 'calibration' (standardization) in analytical chemistry is the operation that determines the functional relationship between measured values (signal intensities, y) and the chemical (sometimes, physical) properties that characterize analytes and their amounts or, more usually, concentrations (x). This operation includes the selection of the model, the estimation of the parameters and, in addition, the errors and their validation.…”

Section: Defining Calibrationmentioning

confidence: 99%

Classical Linear Regression by the Least Squares Method

Andrade-Garda

Carlosena

Soto-Ferreiro

et al. 2013

Basic Chemometric Techniques in Atomic Spectroscopy

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The objective of this chapter is to present some fundamental issues that every atomic spectrometry practitioner should keep in mind when developing a calibration. However familiar this may sound it is definitely a critical step, as important as the development of proper experimental measurement conditions. The fundamentals of the least squares fit are presented and discussed, with special emphasis on the practical limitations we currently experience in laboratories. Many discussions will be devoted to validation of the model and to clarification of some misconceptions that appear sometimes in the literature. The widely applied standard additions method is reviewed and discussed to show that extrapolation is a risky practice that can be avoided very easily. Polynomials of order two (quadratic) provide the analyst with a readily available means of matching points from a curved calibration to a function suitable for interpolation. However, extra caution is necessary to avoid lack of fit, and even small extrapolations beyond the calibrated range are unwise. Finally, two appendixes are included to discuss in some detail the correct use of the Mandel’s test for linearity and how to compare two regression lines (typically, the aqueous calibration fit and the standard additions one).

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“…However, Ortiz et al [35] pointed out that when extrapolating, the analyte concentration is obtained by setting Y 0 ¼ 0 and calculating x 0 ¼Àa SAC =b SAC , but even in this case, the uncertainty of the signal must be included in calculations, leading to…”

Section: Standard Addition Calibrationmentioning

confidence: 99%

Practical Considerations on Indirect Calibration in Analytical Chemistry

González¹

2017

Uncertainty Quantification and Model Calibration

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Indirect or methodological calibration in chemical analysis is outlined. The establishment of calibration curves is introduced and discussed. Linear calibration is presented and considered in three scenarios commonly faced in chemical analysis: external calibration (EC) when there are no matrix effects in the sample analysis; standard addition calibration (SAC) when these effects are present and internal standard calibration (ISC) in cases of intrinsic variability of the analytical signal or possible losses of the analyte in stages prior to the measurement. In each kind of calibration, the uncertainty and confidence interval for the determined analyte concentration are given.

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Quality of Analytical Measurements: Univariate Regression

Cited by 16 publications

References 51 publications

Usefulness of PARAFAC for the Quantification, Identification, and Description of Analytical Data

Usefulness of PARAFAC for the Quantification, Identification, and Description of Analytical Data

Classical Linear Regression by the Least Squares Method

Practical Considerations on Indirect Calibration in Analytical Chemistry

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